# make kable table with consistent formatting
make_table <- function(..., title = "", col_names = c("")) {
title <- paste0("<center><span style = 'font-size:150%;color:black'><b>",
title,
"</span></b><center>")
as_tibble(...) %>%
kbl(caption = title,
col.names = col_names) %>%
kable_material() %>%
row_spec(row=0, background = "#43494C" , color = "white", bold = TRUE)
}## Loading in data
endowment_data <- read_rds(here("data", "endowment_filter_data_990.RDS"))
names <- read_csv(here("data", "companies.csv")) %>%
mutate(EIN = as.character(ein)) %>%
select(-ein)## Rows: 308 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): organization_name
## dbl (2): EIN, ein
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Calculating Percent Spend Down
## Calculating Spend Down, NOT including the CYMs
## 100 - (EYE/BYB * 100)
spend_down_calc1 <- endowment_data %>%
filter(!is.na(CYBeginningYearBalanceAmt)) %>%
mutate(spend_down = 100 - (CYEndYearBalanceAmt/CYBeginningYearBalanceAmt * 100)) %>%
arrange(desc(spend_down)) %>%
select(EIN, CYEndYearBalanceAmt, CYBeginningYearBalanceAmt, spend_down)
spend_down_calc1 %>%
filter(!is.na(spend_down) & spend_down != -Inf) %>%
summarize(avg_spend_down = mean(spend_down),
median_spend_down = median(spend_down),
sd_spend_down = sd(spend_down))
ggplot(spend_down_calc1, aes(x = spend_down)) +
geom_histogram(binwidth = 10) +
xlab("Spend Down") +
ggtitle(label = "Histogram", subtitle = "Spend Down = 100 - (EYB / BYB * 100)")## Calculating Spend Down, NOT including the CYMs
## EYE/BYB * 100
spend_down_calc2 <- endowment_data %>%
filter(!is.na(CYBeginningYearBalanceAmt)) %>%
mutate(spend_down = (CYEndYearBalanceAmt/CYBeginningYearBalanceAmt * 100)) %>%
arrange(desc(spend_down)) %>%
select(EIN, CYEndYearBalanceAmt, CYBeginningYearBalanceAmt, spend_down)
spend_down_calc2 %>%
filter(!is.na(spend_down) & spend_down != -Inf) %>%
summarize(avg_spend_down = mean(spend_down),
median_spend_down = median(spend_down),
sd_spend_down = sd(spend_down))
ggplot(spend_down_calc2, aes(x = spend_down)) +
geom_histogram(binwidth = 10) +
xlab("Spend Down") +
ggtitle(label = "Histogram", subtitle = "Spend Down = (EYB / BYB * 100)")## Calculating Spend Down, NOT including the CYMs
## (EYB - BYB)/BYB * 100
## Rose has notes on her choice for this calculation
spend_down <- endowment_data %>%
filter(!is.na(CYBeginningYearBalanceAmt)) %>%
mutate(spend_down = CYEndYearBalanceAmt - CYBeginningYearBalanceAmt,
pct_spend_down = spend_down/CYBeginningYearBalanceAmt * 100) %>%
arrange(desc(pct_spend_down)) %>%
left_join(names, by = "EIN")
# Basic summary stats
spend_down %>%
filter(!is.na(pct_spend_down) & pct_spend_down != Inf) %>%
summarize(avg_spend_down = mean(pct_spend_down),
median_spend_down = median(pct_spend_down),
sd_spend_down = sd(pct_spend_down))spend_down %>%
filter(!is.na(pct_spend_down) & pct_spend_down != Inf) %>%
group_by(EIN) %>%
summarize(avg_spend_down = mean(pct_spend_down),
median_spend_down = median(pct_spend_down),
sd_spend_down = sd(pct_spend_down))# Basic histogram summarizing it
ggplot(spend_down, aes(x = pct_spend_down)) +
geom_histogram(binwidth = 20) +
xlab("% Spend Down\n(EYB - BYB) / BYB * 100") +
ggtitle(label = "Percentage of Spend Down", subtitle = "Red Line indicates 100%") +
theme_classic() +
geom_vline(xintercept = 100, color = "maroon", linetype = "dotted")spend_down %>%
filter(pct_spend_down != Inf) %>%
select(organization_name, fiscal_year, pct_spend_down) %>%
make_table(title = "Percent Spend Down", col_names = c("Name", "Fiscal Year", "% Spend Down")) %>%
scroll_box(height = "450px")| Name | Fiscal Year | % Spend Down |
|---|---|---|
| Ballet Arizona | 2017 | 3621.7694455 |
| Joffrey Ballet | 2015 | 747.2041559 |
| Ballet Hispanico | 2021 | 432.1555786 |
| First State Ballet Theatre | 2020 | 242.6848638 |
| Atlanta Ballet | 2017 | 207.7002614 |
| Nashville Ballet | 2016 | 206.2101382 |
| Grand Rapids Ballet | 2015 | 186.0427033 |
| Joffrey Ballet | 2019 | 146.9046722 |
| Texas Ballet Theater | 2015 | 133.3333333 |
| Miami City Ballet | 2021 | 103.3796951 |
| Ballet Memphis | 2018 | 100.3575811 |
| Joffrey Ballet | 2020 | 90.8216917 |
| Ballet Austin | 2017 | 90.2783217 |
| Texas Ballet Theater | 2018 | 85.8401625 |
| BalletMet | 2018 | 85.0249761 |
| Atlanta Ballet | 2016 | 81.8387443 |
| First State Ballet Theatre | 2019 | 69.9900000 |
| Richmond Ballet | 2018 | 57.4030070 |
| First State Ballet Theatre | 2016 | 53.2556470 |
| Dayton Ballet | 2019 | 47.3628143 |
| Ballet Des Moines | 2018 | 40.4800000 |
| Richmond Ballet | 2020 | 39.8449675 |
| Eugene Ballet | 2021 | 35.3676599 |
| Richmond Ballet | 2019 | 34.8263002 |
| First State Ballet Theatre | 2017 | 32.2270270 |
| New Mexico Ballet Company | 2019 | 28.7289611 |
| Aspen Santa Fe Ballet | 2017 | 27.9853981 |
| Richmond Ballet | 2017 | 27.4751409 |
| Miami City Ballet | 2018 | 26.9869980 |
| Ballet Memphis | 2017 | 26.3332143 |
| BalletMet | 2019 | 22.9766213 |
| Pittsburgh Ballet Theatre | 2021 | 20.2563462 |
| Joffrey Ballet | 2018 | 20.1054139 |
| San Francisco Ballet | 2017 | 18.7877861 |
| Nashville Ballet | 2017 | 18.7510970 |
| Joffrey Ballet | 2016 | 17.1525667 |
| Grand Rapids Ballet | 2020 | 16.5116257 |
| New York City Ballet | 2018 | 16.3644199 |
| Joffrey Ballet | 2017 | 15.8506700 |
| Houston Ballet | 2017 | 15.6387481 |
| Tulsa Ballet | 2018 | 15.3823425 |
| NA | 2018 | 15.2551406 |
| Aspen Santa Fe Ballet | 2016 | 15.1095158 |
| Pacific Northwest Ballet | 2017 | 15.0137712 |
| American Ballet Theatre | 2017 | 14.9425672 |
| Kansas City Ballet | 2015 | 14.6091861 |
| Tulsa Ballet | 2015 | 14.4923885 |
| Kansas City Ballet | 2019 | 14.2884428 |
| Tulsa Ballet | 2019 | 14.0998602 |
| Atlanta Ballet | 2018 | 13.6920172 |
| New York City Ballet | 2017 | 13.0492346 |
| Tulsa Ballet | 2017 | 12.8725445 |
| American Ballet Theatre | 2019 | 12.8340039 |
| The Tallahassee Ballet | 2015 | 12.7775722 |
| Pittsburgh Ballet Theatre | 2018 | 12.4892577 |
| Alvin Ailey American Dance Theater | 2017 | 11.5365116 |
| Atlanta Ballet | 2015 | 11.3598829 |
| Grand Rapids Ballet | 2019 | 11.2809947 |
| The Charleston Ballet | 2017 | 10.7069912 |
| Charlotte Ballet | 2017 | 10.3333680 |
| NA | 2017 | 9.7115934 |
| Charlotte Ballet | 2015 | 9.1252970 |
| Ballet Austin | 2020 | 8.9689362 |
| American Ballet Theatre | 2020 | 8.9365465 |
| Fort Wayne Ballet | 2017 | 8.7036880 |
| Milwaukee Ballet | 2017 | 8.6823347 |
| Ballet West | 2015 | 8.5436426 |
| Charlotte Ballet | 2016 | 8.5301897 |
| Madison Ballet | 2017 | 8.2709975 |
| Ballet West | 2020 | 8.0887426 |
| Charlotte Ballet | 2018 | 8.0057600 |
| NA | 2019 | 7.8186556 |
| The Charleston Ballet | 2018 | 7.7078009 |
| Kansas City Ballet | 2017 | 7.6710943 |
| Miami City Ballet | 2017 | 7.4251152 |
| Tulsa Ballet | 2020 | 7.2630153 |
| Pittsburgh Ballet Theatre | 2017 | 7.1356111 |
| NA | 2020 | 6.8383787 |
| Grand Rapids Ballet | 2017 | 6.7800620 |
| The Tallahassee Ballet | 2017 | 6.6393657 |
| NA | 2016 | 6.4755945 |
| The Sarasota Ballet | 2017 | 6.4328584 |
| Ballet Des Moines | 2019 | 6.2713554 |
| Pacific Northwest Ballet | 2018 | 6.0980960 |
| Grand Rapids Ballet | 2018 | 5.6995093 |
| The Sarasota Ballet | 2020 | 5.3797096 |
| Milwaukee Ballet | 2018 | 5.3251625 |
| Pacific Northwest Ballet | 2020 | 5.2552006 |
| Tulsa Ballet | 2016 | 5.1996153 |
| Madison Ballet | 2018 | 5.1952964 |
| San Francisco Ballet | 2018 | 5.0127410 |
| Ballet Hispanico | 2017 | 4.8257031 |
| Ballet Austin | 2018 | 4.6407201 |
| Oregon Ballet Theatre | 2019 | 4.1823644 |
| American Ballet Theatre | 2015 | 3.9081263 |
| Alvin Ailey American Dance Theater | 2019 | 3.5551869 |
| Houston Ballet | 2018 | 3.5239698 |
| The Tallahassee Ballet | 2019 | 3.4501860 |
| Oregon Ballet Theatre | 2017 | 3.4233093 |
| The Tallahassee Ballet | 2018 | 3.2518997 |
| Ballet Quad Cities | 2017 | 3.2100411 |
| Nashville Ballet | 2018 | 3.1497842 |
| Houston Ballet | 2019 | 3.0126539 |
| Ballet West | 2016 | 2.9479559 |
| Pennsylvania Ballet | 2017 | 2.7458153 |
| Ballet Austin | 2019 | 2.7039028 |
| Alvin Ailey American Dance Theater | 2018 | 2.6520142 |
| Pacific Northwest Ballet | 2019 | 2.5299841 |
| Eugene Ballet | 2020 | 2.0266667 |
| Alvin Ailey American Dance Theater | 2015 | 2.0168085 |
| Oregon Ballet Theatre | 2020 | 1.9913020 |
| Oregon Ballet Theatre | 2021 | 1.5619387 |
| Ballet Hispanico | 2019 | 1.5405288 |
| Nashville Ballet | 2015 | 1.3707995 |
| Kansas City Ballet | 2016 | 1.3182366 |
| Miami City Ballet | 2019 | 1.1650569 |
| Alvin Ailey American Dance Theater | 2020 | 1.1173579 |
| Madison Ballet | 2020 | 1.0503166 |
| Pennsylvania Ballet | 2020 | 0.9848159 |
| Nevada Ballet Theatre | 2018 | 0.9818432 |
| Fort Wayne Ballet | 2018 | 0.9437819 |
| Pittsburgh Ballet Theatre | 2019 | 0.8461817 |
| Pennsylvania Ballet | 2018 | 0.8212002 |
| Nashville Ballet | 2020 | 0.6340668 |
| Pacific Northwest Ballet | 2015 | 0.6277229 |
| The Alabama Ballet | 2020 | 0.5951924 |
| Texas Ballet Theater | 2019 | 0.5813097 |
| Texas Ballet Theater | 2020 | 0.5742863 |
| Colorado Ballet | 2015 | 0.5500000 |
| Fort Wayne Ballet | 2019 | 0.4949803 |
| Ballet Hispanico | 2018 | 0.4786320 |
| Milwaukee Ballet | 2015 | 0.2558318 |
| New Mexico Ballet Company | 2020 | 0.2184921 |
| Oregon Ballet Theatre | 2018 | 0.1654999 |
| Ballet Hispanico | 2016 | 0.1309318 |
| New York City Ballet | 2019 | 0.0809932 |
| Texas Ballet Theater | 2017 | 0.0285424 |
| Texas Ballet Theater | 2016 | 0.0199837 |
| Dance Theatre of Harlem | 2015 | 0.0000000 |
| The Sarasota Ballet | 2015 | 0.0000000 |
| BalletMet | 2015 | 0.0000000 |
| Ballet Arizona | 2015 | 0.0000000 |
| BalletMet | 2016 | 0.0000000 |
| San Francisco Ballet | 2015 | 0.0000000 |
| American Repertory Ballet | 2016 | 0.0000000 |
| Eugene Ballet | 2016 | 0.0000000 |
| The Alabama Ballet | 2016 | 0.0000000 |
| Ballet Arizona | 2016 | 0.0000000 |
| Dance Theatre of Harlem | 2016 | 0.0000000 |
| San Francisco Ballet | 2016 | 0.0000000 |
| BalletMet | 2017 | 0.0000000 |
| The Alabama Ballet | 2017 | 0.0000000 |
| Dance Theatre of Harlem | 2017 | 0.0000000 |
| American Repertory Ballet | 2017 | 0.0000000 |
| Dance Theatre of Harlem | 2018 | 0.0000000 |
| The Washington Ballet | 2018 | 0.0000000 |
| The Alabama Ballet | 2018 | 0.0000000 |
| Eugene Ballet | 2018 | 0.0000000 |
| Eugene Ballet | 2019 | 0.0000000 |
| Aspen Santa Fe Ballet | 2019 | 0.0000000 |
| Dance Theatre of Harlem | 2019 | 0.0000000 |
| Aspen Santa Fe Ballet | 2020 | 0.0000000 |
| BalletMet | 2020 | 0.0000000 |
| The Washington Ballet | 2020 | 0.0000000 |
| Dance Theatre of Harlem | 2020 | 0.0000000 |
| NA | 2015 | -0.0388779 |
| Nevada Ballet Theatre | 2020 | -0.0958192 |
| Milwaukee Ballet | 2019 | -0.2192253 |
| Ballet Hispanico | 2020 | -0.2921837 |
| Ballet Arizona | 2018 | -0.2958794 |
| The Washington Ballet | 2015 | -0.3307636 |
| Nevada Ballet Theatre | 2015 | -0.4171208 |
| San Francisco Ballet | 2019 | -0.4505953 |
| Houston Ballet | 2020 | -0.5394078 |
| Aspen Santa Fe Ballet | 2015 | -0.6154796 |
| Madison Ballet | 2019 | -0.6341596 |
| Pennsylvania Ballet | 2019 | -0.6773370 |
| Nevada Ballet Theatre | 2016 | -0.7510343 |
| Nevada Ballet Theatre | 2021 | -0.8682968 |
| Houston Ballet | 2015 | -0.9290425 |
| American Ballet Theatre | 2016 | -0.9486236 |
| Nevada Ballet Theatre | 2019 | -0.9524021 |
| Ballet West | 2018 | -1.0492992 |
| New York City Ballet | 2015 | -1.0935295 |
| Charlotte Ballet | 2019 | -1.1038242 |
| Nevada Ballet Theatre | 2017 | -1.1116605 |
| Milwaukee Ballet | 2016 | -1.1445611 |
| Ballet Hispanico | 2015 | -1.2189376 |
| Ballet Arizona | 2019 | -1.3078878 |
| Ballet Arizona | 2020 | -1.3712710 |
| The Charleston Ballet | 2016 | -1.5786659 |
| Madison Ballet | 2016 | -1.6090293 |
| Fort Wayne Ballet | 2020 | -1.6147683 |
| Pennsylvania Ballet | 2015 | -2.1126469 |
| The Charleston Ballet | 2015 | -2.8140235 |
| New York City Ballet | 2020 | -2.9450705 |
| American Ballet Theatre | 2018 | -3.0002561 |
| Charlotte Ballet | 2020 | -3.0415463 |
| American Ballet Theatre | 2014 | -3.2115030 |
| Pittsburgh Ballet Theatre | 2020 | -3.3144664 |
| Grand Rapids Ballet | 2016 | -3.4832176 |
| Ballet West | 2017 | -3.5593957 |
| Ballet Austin | 2016 | -3.6649665 |
| The Alabama Ballet | 2019 | -3.6897295 |
| Kansas City Ballet | 2018 | -3.7141912 |
| The Tallahassee Ballet | 2020 | -4.1994951 |
| Pennsylvania Ballet | 2016 | -4.2562307 |
| Ballet Quad Cities | 2020 | -4.5315904 |
| Miami City Ballet | 2020 | -4.8106966 |
| Houston Ballet | 2016 | -4.9809760 |
| Ballet Memphis | 2015 | -5.1492312 |
| The Tallahassee Ballet | 2016 | -5.8413323 |
| The Sarasota Ballet | 2018 | -5.8747682 |
| Ballet Memphis | 2016 | -5.9367915 |
| Ballet Memphis | 2019 | -6.1694703 |
| Alvin Ailey American Dance Theater | 2016 | -7.2545116 |
| Pacific Northwest Ballet | 2016 | -7.3246969 |
| New York City Ballet | 2016 | -7.4654042 |
| Madison Ballet | 2015 | -7.5915755 |
| San Francisco Ballet | 2020 | -8.4597794 |
| Pittsburgh Ballet Theatre | 2015 | -9.7519131 |
| Ballet Quad Cities | 2016 | -9.9700000 |
| Ballet Memphis | 2020 | -11.6985509 |
| Pittsburgh Ballet Theatre | 2016 | -13.8628328 |
| The Sarasota Ballet | 2019 | -15.1497071 |
| The Washington Ballet | 2016 | -25.5508697 |
| Atlanta Ballet | 2020 | -28.5158513 |
| Atlanta Ballet | 2019 | -36.6916144 |
| The Washington Ballet | 2017 | -48.7379222 |
| First State Ballet Theatre | 2015 | -49.5289330 |
| The Washington Ballet | 2019 | -50.1144953 |
| First State Ballet Theatre | 2018 | -72.7468454 |
| Orlando Ballet | 2020 | -90.9983829 |
| Nashville Ballet | 2019 | -94.4004512 |
(End Year Balance - Beginning Year Balance) / Beginning Year Balance * 100
If EYB is larger, positive result. Meaning there was a INCREASE in total funds.
If BYB is larger, negative result. Meaning a DECREASE in total funds. If result is above 100, the fund was at least DOUBLED.
## Spend Down over Time
spend_down_plot <- spend_down %>%
ggplot(aes(x = fiscal_year, y = pct_spend_down,
group = organization_name, color = organization_name)) +
geom_line(alpha = 0.5) +
theme_bw() +
labs(y = "Percent Spend Down",
x = "Fiscal Year",
title = "Percentage of Endowment Spend Down",
subtitle = "By Fiscal Year") +
theme(plot.title = element_text(size = 10, face = "bold", hjust = .5),
axis.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 5, face = "italic", hjust = .5),
axis.text.x = element_text(size = 10, angle = 25),
strip.text = element_text(face="bold",size = 5),
legend.key.size = unit(1, 'mm'),
legend.text = element_text(size=7)) +
scale_y_continuous(labels = scales::comma_format(),
breaks = scales::pretty_breaks(n = 20)) +
geom_hline(yintercept = 100, linetype = "dotted", color = "gray")
ggplotly(spend_down_plot) %>%
layout(showlegend = FALSE)## Spend Down over Time
### WITHOUT ARIAZONA
spend_down_plot <- spend_down %>%
filter(organization_name != "Ballet Arizona") %>%
ggplot(aes(x = fiscal_year, y = pct_spend_down,
group = organization_name, color = organization_name)) +
geom_line(show.legend = FALSE, alpha = 0.5) +
theme_bw() +
labs(y = "Percent Spend Down",
x = "Fiscal Year",
title = "Percentage of Endowment Spend Down",
subtitle = "By Fiscal Year") +
theme(plot.title = element_text(size = 10, face = "bold", hjust = .5),
axis.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 5, face = "italic", hjust = .5),
axis.text.x = element_text(size = 10, angle = 25),
strip.text = element_text(face="bold",size = 5),
legend.key.size = unit(1, 'mm'),
legend.text = element_text(size=7)) +
scale_y_continuous(labels = scales::comma_format(),
breaks = scales::breaks_pretty(n = 20)) +
geom_hline(yintercept = 100, linetype = "dotted", color = "gray")
ggplotly(spend_down_plot) %>%
layout(showlegend = FALSE)##Plot with Y scale between -100 and 100
limited_scale <- spend_down %>%
ggplot(aes(x = fiscal_year, y = pct_spend_down,
group = organization_name, color = organization_name)) +
geom_line(show.legend = FALSE, alpha = 0.5) +
theme_bw() +
labs(y = "Percent Spend Down",
x = "Fiscal Year",
title = "Percentage of Endowment Spend Down (max 100)",
subtitle = "By Fiscal Year") +
theme(plot.title = element_text(size = 10, face = "bold", hjust = .5),
axis.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 5, face = "italic", hjust = .5),
axis.text.x = element_text(size = 10, angle = 25),
strip.text = element_text(face="bold",size = 5),
legend.key.size = unit(1, 'mm'),
legend.text = element_text(size=7)) +
scale_y_continuous(labels = scales::comma_format(),
breaks = scales::pretty_breaks(n = 20),
limits = c(-100, 100))
ggplotly(limited_scale) %>%
layout(showlegend = FALSE)Within the Pandemic
## Pandemic Years
spend_down_plot <- spend_down %>%
filter(fiscal_year %in% c("2018", "2019", "2020", "2021", "2022")) %>%
ggplot(aes(x = fiscal_year, y = pct_spend_down,
group = organization_name, color = organization_name)) +
geom_line(show.legend = FALSE, alpha = 0.5) +
theme_bw() +
labs(y = "Percent Spend Down",
x = "Fiscal Year",
title = "Percentage of Endowment Spend Down",
subtitle = "Within Pandemic Years") +
theme(plot.title = element_text(size = 10, face = "bold", hjust = .5),
axis.title = element_text(size = 12, face = "bold"),
plot.subtitle = element_text(size = 5, face = "italic", hjust = .5),
axis.text.x = element_text(size = 10, angle = 25),
strip.text = element_text(face="bold",size = 5),
legend.key.size = unit(1, 'mm'),
legend.text = element_text(size=7)) +
scale_y_continuous(labels = scales::comma_format(),
breaks = scales::pretty_breaks(n = 20)) +
geom_hline(yintercept = 100, linetype = "dotted", color = "gray")
ggplotly(spend_down_plot) %>%
layout(showlegend = FALSE)## Table of available in-pandemic data
spend_down %>%
filter(fiscal_year %in% c("2019", "2020", "2021", "2022")) %>%
select(organization_name, pct_spend_down, fiscal_year) %>%
arrange(desc(fiscal_year)) %>%
make_table(title = "Percent Spend Down within Pandemic Years", col_names = c("Name", "% Spend Down", "Year")) %>%
scroll_box(height = "450px")| Name | % Spend Down | Year |
|---|---|---|
| Ballet Hispanico | 432.1555786 | 2021 |
| Miami City Ballet | 103.3796951 | 2021 |
| Eugene Ballet | 35.3676599 | 2021 |
| Pittsburgh Ballet Theatre | 20.2563462 | 2021 |
| Oregon Ballet Theatre | 1.5619387 | 2021 |
| Nevada Ballet Theatre | -0.8682968 | 2021 |
| First State Ballet Theatre | 242.6848638 | 2020 |
| Joffrey Ballet | 90.8216917 | 2020 |
| Richmond Ballet | 39.8449675 | 2020 |
| Grand Rapids Ballet | 16.5116257 | 2020 |
| Ballet Austin | 8.9689362 | 2020 |
| American Ballet Theatre | 8.9365465 | 2020 |
| Ballet West | 8.0887426 | 2020 |
| Tulsa Ballet | 7.2630153 | 2020 |
| NA | 6.8383787 | 2020 |
| The Sarasota Ballet | 5.3797096 | 2020 |
| Pacific Northwest Ballet | 5.2552006 | 2020 |
| Eugene Ballet | 2.0266667 | 2020 |
| Oregon Ballet Theatre | 1.9913020 | 2020 |
| Alvin Ailey American Dance Theater | 1.1173579 | 2020 |
| Madison Ballet | 1.0503166 | 2020 |
| Pennsylvania Ballet | 0.9848159 | 2020 |
| Nashville Ballet | 0.6340668 | 2020 |
| The Alabama Ballet | 0.5951924 | 2020 |
| Texas Ballet Theater | 0.5742863 | 2020 |
| New Mexico Ballet Company | 0.2184921 | 2020 |
| Aspen Santa Fe Ballet | 0.0000000 | 2020 |
| BalletMet | 0.0000000 | 2020 |
| The Washington Ballet | 0.0000000 | 2020 |
| Dance Theatre of Harlem | 0.0000000 | 2020 |
| Nevada Ballet Theatre | -0.0958192 | 2020 |
| Ballet Hispanico | -0.2921837 | 2020 |
| Houston Ballet | -0.5394078 | 2020 |
| Ballet Arizona | -1.3712710 | 2020 |
| Fort Wayne Ballet | -1.6147683 | 2020 |
| New York City Ballet | -2.9450705 | 2020 |
| Charlotte Ballet | -3.0415463 | 2020 |
| Pittsburgh Ballet Theatre | -3.3144664 | 2020 |
| The Tallahassee Ballet | -4.1994951 | 2020 |
| Ballet Quad Cities | -4.5315904 | 2020 |
| Miami City Ballet | -4.8106966 | 2020 |
| San Francisco Ballet | -8.4597794 | 2020 |
| Ballet Memphis | -11.6985509 | 2020 |
| Atlanta Ballet | -28.5158513 | 2020 |
| Orlando Ballet | -90.9983829 | 2020 |
| Joffrey Ballet | 146.9046722 | 2019 |
| First State Ballet Theatre | 69.9900000 | 2019 |
| Dayton Ballet | 47.3628143 | 2019 |
| Richmond Ballet | 34.8263002 | 2019 |
| New Mexico Ballet Company | 28.7289611 | 2019 |
| BalletMet | 22.9766213 | 2019 |
| Kansas City Ballet | 14.2884428 | 2019 |
| Tulsa Ballet | 14.0998602 | 2019 |
| American Ballet Theatre | 12.8340039 | 2019 |
| Grand Rapids Ballet | 11.2809947 | 2019 |
| NA | 7.8186556 | 2019 |
| Ballet Des Moines | 6.2713554 | 2019 |
| Oregon Ballet Theatre | 4.1823644 | 2019 |
| Alvin Ailey American Dance Theater | 3.5551869 | 2019 |
| The Tallahassee Ballet | 3.4501860 | 2019 |
| Houston Ballet | 3.0126539 | 2019 |
| Ballet Austin | 2.7039028 | 2019 |
| Pacific Northwest Ballet | 2.5299841 | 2019 |
| Ballet Hispanico | 1.5405288 | 2019 |
| Miami City Ballet | 1.1650569 | 2019 |
| Pittsburgh Ballet Theatre | 0.8461817 | 2019 |
| Texas Ballet Theater | 0.5813097 | 2019 |
| Fort Wayne Ballet | 0.4949803 | 2019 |
| New York City Ballet | 0.0809932 | 2019 |
| Eugene Ballet | 0.0000000 | 2019 |
| Aspen Santa Fe Ballet | 0.0000000 | 2019 |
| Dance Theatre of Harlem | 0.0000000 | 2019 |
| Milwaukee Ballet | -0.2192253 | 2019 |
| San Francisco Ballet | -0.4505953 | 2019 |
| Madison Ballet | -0.6341596 | 2019 |
| Pennsylvania Ballet | -0.6773370 | 2019 |
| Nevada Ballet Theatre | -0.9524021 | 2019 |
| Charlotte Ballet | -1.1038242 | 2019 |
| Ballet Arizona | -1.3078878 | 2019 |
| The Alabama Ballet | -3.6897295 | 2019 |
| Ballet Memphis | -6.1694703 | 2019 |
| The Sarasota Ballet | -15.1497071 | 2019 |
| Atlanta Ballet | -36.6916144 | 2019 |
| The Washington Ballet | -50.1144953 | 2019 |
| Nashville Ballet | -94.4004512 | 2019 |
spend_down %>%
filter(fiscal_year %in% c("2019", "2020", "2021", "2022")) %>%
select(organization_name, pct_spend_down, fiscal_year) %>%
group_by(fiscal_year) %>%
summarize(total_in_year = n())Range of Endowment Spend Down
## Ranges of different spend-downs
# reorder(organization_name, pull(summarize(spend_down, sd = sd(group_by(spend_down, pct_spend_down)))), na.rm = TRUE)
# Reordering by standard deviation of pct_spend down
spend_down_box <- spend_down %>%
group_by(organization_name) %>%
filter(pct_spend_down != Inf) %>%
summarize(sd = sd(pct_spend_down, na.rm = TRUE)) %>%
right_join(spend_down, by = "organization_name") %>%
select(organization_name, EIN, pct_spend_down, sd) %>%
mutate(organization_name = reorder(organization_name, -sd, na.rm = TRUE))
## Unlimited
box_plot <- ggplot(spend_down_box, aes(x = organization_name, y = pct_spend_down)) +
geom_boxplot(aes(color = organization_name), show.legend = FALSE) +
geom_point(size = 1, alpha = 0.5) +
theme_bw() +
labs(title = "Range of Endowment Spend Downper Company",
x = "Dance Company",
y = "Percent of Endowment Spend Down") +
theme(axis.text.x = element_blank()) +
geom_hline(yintercept = 100, linetype = "dotted", color = "maroon")
ggplotly(box_plot) %>%
layout(showlegend = FALSE)##Limited to 100 for visibility
box_plot_lim <- ggplot(spend_down_box, aes(x = organization_name, y = pct_spend_down)) +
geom_boxplot(aes(color = organization_name), show.legend = FALSE) +
geom_point(size = 1, alpha = 0.5) +
theme_bw() +
labs(title = "Range of Endowment Spend Down (Max of 100) per Company",
x = "Dance Company",
y = "Percent of Endowment Spend Down") +
theme(axis.text.x = element_blank()) +
scale_y_continuous(breaks = scales::breaks_pretty(n = 20),
limit = c(-100,100))
ggplotly(box_plot_lim) %>%
layout(showlegend = FALSE)